


'''simple docstring'''







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=2   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=10   ,						snake_case__=3   ,						snake_case__=32 * 8   ,						snake_case__=32 * 8   ,						snake_case__=4   ,						snake_case__=64   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							parent
            _lowerCAmelCase     :				Dict       =							batch_size
            _lowerCAmelCase     :				Any       =							is_training
            _lowerCAmelCase     :				Tuple       =							use_auxiliary_loss
            _lowerCAmelCase     :				int       =							num_queries
            _lowerCAmelCase     :				Any       =							num_channels
            _lowerCAmelCase     :				str       =							min_size
            _lowerCAmelCase     :				Optional[int]       =							max_size
            _lowerCAmelCase     :				str       =							num_labels
            _lowerCAmelCase     :				Any       =							hidden_dim
            _lowerCAmelCase     :				int       =							hidden_dim







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
                snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							torch.ones([self.batch_size, self.min_size, self.max_size]   ,						device=snake_case__ )

            _lowerCAmelCase     :				List[str]       =							(
                torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,						device=snake_case__ ) > 0.5
            ).float()
            _lowerCAmelCase     :				Dict       =							(torch.rand((self.batch_size, self.num_labels)   ,						device=snake_case__ ) > 0.5).long()

            _lowerCAmelCase     :				Union[str, Any]       =							self.get_config()
            return config, pixel_values, pixel_mask, mask_labels, class_labels







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							MaskaFormerConfig(
                hidden_size=self.hidden_dim   ,						)
            _lowerCAmelCase     :				Dict       =							self.num_queries
            _lowerCAmelCase     :				int       =							self.num_labels

            _lowerCAmelCase     :				List[str]       =							[1, 1, 1, 1]
            _lowerCAmelCase     :				Dict       =							self.num_channels

            _lowerCAmelCase     :				Optional[Any]       =							64
            _lowerCAmelCase     :				Tuple       =							128
            _lowerCAmelCase     :				List[Any]       =							self.hidden_dim
            _lowerCAmelCase     :				Union[str, Any]       =							self.hidden_dim
            _lowerCAmelCase     :				List[Any]       =							self.hidden_dim
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							self.prepare_config_and_inputs()
            _lowerCAmelCase     :				Any       =							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
            return config, inputs_dict







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							output.encoder_hidden_states
            _lowerCAmelCase     :				Optional[Any]       =							output.pixel_decoder_hidden_states
            _lowerCAmelCase     :				Any       =							output.transformer_decoder_hidden_states

            self.parent.assertTrue(len(snake_case__ )   ,						len(config.backbone_config.depths ) )
            self.parent.assertTrue(len(snake_case__ )   ,						len(config.backbone_config.depths ) )
            self.parent.assertTrue(len(snake_case__ )   ,						config.decoder_layers )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__=False ):

            '''simple docstring'''

            with torch.no_grad():
                   _lowerCAmelCase     :				Optional[Any]       =							MaskaFormerModel(config=snake_case__ )
                   model.to(snake_case__ )
                   model.eval()

                   _lowerCAmelCase     :				Dict       =							model(pixel_values=snake_case__   ,						pixel_mask=snake_case__ )
                   _lowerCAmelCase     :				Dict       =							model(snake_case__   ,						output_hidden_states=snake_case__ )

            self.parent.assertEqual(
                output.transformer_decoder_last_hidden_state.shape   ,						(self.batch_size, self.num_queries, self.hidden_dim)   ,						)
            # let's ensure the other two hidden state exists
            self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
            self.parent.assertTrue(output.encoder_last_hidden_state is not None )

            if output_hidden_states:
                   self.check_output_hidden_state(snake_case__   ,						snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							MaskaFormerForUniversalSegmentation(config=snake_case__ )
            model.to(snake_case__ )
            model.eval()

            def comm_check_on_output(snake_case__ ):
                   # let's still check that all the required stuff is there
                   self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
                   self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
                   self.parent.assertTrue(result.encoder_last_hidden_state is not None )
                   # okay, now we need to check the logits shape
                   # due to the encoder compression, masks have a //4 spatial size
                   self.parent.assertEqual(
                       result.masks_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,						)
                   # + 1 for null class
                   self.parent.assertEqual(
                       result.class_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.num_labels + 1) )

            with torch.no_grad():
                   _lowerCAmelCase     :				Optional[Any]       =							model(pixel_values=snake_case__   ,						pixel_mask=snake_case__ )
                   _lowerCAmelCase     :				Dict       =							model(snake_case__ )

                   comm_check_on_output(snake_case__ )

                   _lowerCAmelCase     :				Any       =							model(
                       pixel_values=snake_case__   ,						pixel_mask=snake_case__   ,						mask_labels=snake_case__   ,						class_labels=snake_case__ )

            comm_check_on_output(snake_case__ )

            self.parent.assertTrue(result.loss is not None )
            self.parent.assertEqual(result.loss.shape   ,						torch.Size([1] ) )
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
     __magic_name__           =     {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							MaskaFormerModelTester(self )
            _lowerCAmelCase     :				Optional[Any]       =							ConfigTester(self   ,						config_class=snake_case__   ,						has_text_modality=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(snake_case__   ,						**snake_case__   ,						output_hidden_states=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case__ )







     @unittest.skip(reason='Mask2Former does not use inputs_embeds' )
     def a						(			self ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
     def a						(			self ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='Mask2Former is not a generative model' )
     def a						(			self ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='Mask2Former does not use token embeddings' )
     def a						(			self ):

            '''simple docstring'''

            pass







     @require_torch_multi_gpu
     @unittest.skip(
         reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
     def a						(			self ):

            '''simple docstring'''

            pass







     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase     :				int       =							model_class(snake_case__ )
                   _lowerCAmelCase     :				str       =							inspect.signature(model.forward )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _lowerCAmelCase     :				str       =							[*signature.parameters.keys()]

                   _lowerCAmelCase     :				Optional[Any]       =							['pixel_values']
                   self.assertListEqual(arg_names[:1]   ,						snake_case__ )







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                   _lowerCAmelCase     :				int       =							MaskaFormerModel.from_pretrained(snake_case__ )
                   self.assertIsNotNone(snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							(self.model_tester.min_size,) * 2
            _lowerCAmelCase     :				Union[str, Any]       =							{
                'pixel_values': torch.randn((2, 3, *size)   ,						device=snake_case__ ),
                'mask_labels': torch.randn((2, 10, *size)   ,						device=snake_case__ ),
                'class_labels': torch.zeros(2   ,						10   ,						device=snake_case__ ).long(),
            }
            _lowerCAmelCase     :				List[str]       =							self.model_tester.get_config()

            _lowerCAmelCase     :				str       =							MaskaFormerForUniversalSegmentation(snake_case__ ).to(snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							model(**snake_case__ )
            self.assertTrue(outputs.loss is not None )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskaformer_model(snake_case__   ,						**snake_case__   ,						output_hidden_states=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase     :				Any       =							model_class(snake_case__ ).to(snake_case__ )
                   _lowerCAmelCase     :				Dict       =							model(**snake_case__   ,						output_attentions=snake_case__ )
                   self.assertTrue(outputs.attentions is not None )







     def a						(			self ):

            '''simple docstring'''

            if not self.model_tester.is_training:
                   return

            _lowerCAmelCase     :				str       =							self.all_model_classes[1]
            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.model_tester.prepare_config_and_inputs()

            _lowerCAmelCase     :				Any       =							model_class(snake_case__ )
            model.to(snake_case__ )
            model.train()

            _lowerCAmelCase     :				Tuple       =							model(snake_case__   ,						mask_labels=snake_case__   ,						class_labels=snake_case__ ).loss
            loss.backward()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.all_model_classes[1]
            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							self.model_tester.prepare_config_and_inputs()
            _lowerCAmelCase     :				Optional[Any]       =							True
            _lowerCAmelCase     :				Dict       =							True

            _lowerCAmelCase     :				Union[str, Any]       =							model_class(snake_case__ ).to(snake_case__ )
            model.train()

            _lowerCAmelCase     :				Optional[int]       =							model(snake_case__   ,						mask_labels=snake_case__   ,						class_labels=snake_case__ )

            _lowerCAmelCase     :				Any       =							outputs.encoder_hidden_states[0]
            encoder_hidden_states.retain_grad()

            _lowerCAmelCase     :				Optional[Any]       =							outputs.pixel_decoder_hidden_states[0]
            pixel_decoder_hidden_states.retain_grad()

            _lowerCAmelCase     :				Any       =							outputs.transformer_decoder_hidden_states[0]
            transformer_decoder_hidden_states.retain_grad()

            _lowerCAmelCase     :				Any       =							outputs.attentions[0]
            attentions.retain_grad()

            outputs.loss.backward(retain_graph=snake_case__ )

            self.assertIsNotNone(encoder_hidden_states.grad )
            self.assertIsNotNone(pixel_decoder_hidden_states.grad )
            self.assertIsNotNone(transformer_decoder_hidden_states.grad )
            self.assertIsNotNone(attentions.grad )
lowerCAmelCase     :  int											=    1e-4






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				str       =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image
@require_vision
@slow
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @cached_property
     def a						(			self ):

            '''simple docstring'''

            return "facebook/mask2former-swin-small-coco-instance"







     @cached_property
     def a						(			self ):

            '''simple docstring'''

            return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(snake_case__ )
            _lowerCAmelCase     :				Any       =							self.default_image_processor
            _lowerCAmelCase     :				Tuple       =							prepare_img()
            _lowerCAmelCase     :				List[Any]       =							image_processor(snake_case__   ,						return_tensors='pt' ).to(snake_case__ )
            _lowerCAmelCase     :				List[str]       =							inputs['pixel_values'].shape
            # check size is divisible by 32
            self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
            # check size
            self.assertEqual(snake_case__   ,						(1, 3, 384, 384) )

            with torch.no_grad():
                   _lowerCAmelCase     :				Any       =							model(**snake_case__ )

            _lowerCAmelCase     :				Any       =							torch.tensor(
                [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(snake_case__ )
            self.assertTrue(
                torch.allclose(
                    outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,						snake_case__   ,						atol=snake_case__ ) )

            _lowerCAmelCase     :				Optional[int]       =							torch.tensor(
                [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(snake_case__ )
            self.assertTrue(
                torch.allclose(
                    outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,						snake_case__   ,						atol=snake_case__ ) )

            _lowerCAmelCase     :				List[Any]       =							torch.tensor(
                [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(snake_case__ )
            self.assertTrue(
                torch.allclose(
                    outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,						snake_case__   ,						atol=snake_case__ ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(snake_case__ ).eval()
            _lowerCAmelCase     :				int       =							self.default_image_processor
            _lowerCAmelCase     :				str       =							prepare_img()
            _lowerCAmelCase     :				Dict       =							image_processor(snake_case__   ,						return_tensors='pt' ).to(snake_case__ )
            _lowerCAmelCase     :				int       =							inputs['pixel_values'].shape
            # check size is divisible by 32
            self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
            # check size
            self.assertEqual(snake_case__   ,						(1, 3, 384, 384) )

            with torch.no_grad():
                   _lowerCAmelCase     :				List[Any]       =							model(**snake_case__ )
            # masks_queries_logits
            _lowerCAmelCase     :				str       =							outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape   ,						(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
            _lowerCAmelCase     :				Union[str, Any]       =							[
                [-8.7839, -9.0056, -8.8121],
                [-7.4104, -7.0313, -6.5401],
                [-6.6105, -6.3427, -6.4675],
            ]
            _lowerCAmelCase     :				int       =							torch.tensor(snake_case__ ).to(snake_case__ )
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						snake_case__   ,						atol=snake_case__ ) )
            # class_queries_logits
            _lowerCAmelCase     :				str       =							outputs.class_queries_logits
            self.assertEqual(class_queries_logits.shape   ,						(1, model.config.num_queries, model.config.num_labels + 1) )
            _lowerCAmelCase     :				Dict       =							torch.tensor(
                [
                    [1.8324, -8.0835, -4.1922],
                    [0.8450, -9.0050, -3.6053],
                    [0.3045, -7.7293, -3.0275],
                ] ).to(snake_case__ )
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						snake_case__   ,						atol=snake_case__ ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(snake_case__ ).eval()
            _lowerCAmelCase     :				int       =							self.default_image_processor

            _lowerCAmelCase     :				Optional[int]       =							image_processor(
                [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )]   ,						segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )]   ,						return_tensors='pt'   ,						)

            _lowerCAmelCase     :				Optional[Any]       =							inputs['pixel_values'].to(snake_case__ )
            _lowerCAmelCase     :				Any       =							[el.to(snake_case__ ) for el in inputs['mask_labels']]
            _lowerCAmelCase     :				List[Any]       =							[el.to(snake_case__ ) for el in inputs['class_labels']]

            with torch.no_grad():
                   _lowerCAmelCase     :				List[Any]       =							model(**snake_case__ )

            self.assertTrue(outputs.loss is not None )





from unittest.mock import Mock, patch

from file_transfer.send_file import send_file
@patch("socket.socket"	)
@patch("builtins.open"	)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str	)->		int:
			# ===== initialization =====
			UpperCamelCase__       =							Mock()
			UpperCamelCase__       =							conn, Mock()
			UpperCamelCase__       =							iter([1, None]	)
			UpperCamelCase__       =							lambda _A	: next(_A	)

			# ===== invoke =====
			send_file(filename="mytext.txt"					,  testing=_A	)

			# ===== ensurance =====
			sock.assert_called_once()
			sock.return_value.bind.assert_called_once()
			sock.return_value.listen.assert_called_once()
			sock.return_value.accept.assert_called_once()
			conn.recv.assert_called_once()

			file.return_value.__enter__.assert_called_once()
			file.return_value.__enter__.return_value.read.assert_called()

			conn.send.assert_called_once()
			conn.close.assert_called_once()
			sock.return_value.shutdown.assert_called_once()
			sock.return_value.close.assert_called_once()


